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Temporal Heterogeneity of Water Quality in Rural Alabama Water Supplies
Author(s) -
Wedgworth Jessica C.,
Brown Joe,
Olson Julie B.,
Johnson Pauline,
Elliott Mark,
Grammer Phillip,
Stauber Christine E.
Publication year - 2015
Publication title -
journal ‐ american water works association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 74
eISSN - 1551-8833
pISSN - 0003-150X
DOI - 10.5942/jawwa.2015.107.0098
Subject(s) - environmental science , water quality , turbidity , sampling (signal processing) , spatial variability , seasonality , hydrology (agriculture) , spatial distribution , statistics , ecology , mathematics , biology , computer science , geotechnical engineering , filter (signal processing) , engineering , computer vision
Temporal and spatial trends for key water quality measures were evaluated in 12 rural drinking water systems within a three‐county study area in Alabama. The water systems varied in size from very small (25–500 people served) to large (10,001–100,000 people served). Large‐volume water samples were collected from 10 diverse locations within each system on three sampling dates. Sampling locations were assigned to one of five location categories: well, post‐treatment, post‐storage, in‐line, and end‐line. Water quality parameters (i.e., free and total chlorine, pH, turbidity, pressure, heterotrophic plate count) and microbial indicators (i.e., total coliforms, Escherichia coli, Enterococci , male‐specific coliphages) were analyzed for spatial and temporal trends. Analysis of the samples from these rural water systems over nine months did not show a statistically significant association between distribution system sampling locations and water quality measures or microbial indicators. Temporal trends were consistent across sampling locations and were stronger than trends in spatial variability. However, substantial temporal heterogeneity of water quality measures was noted, potentially the result of seasonality, temperature fluctuations, and distribution system operation and maintenance practices. The study results indicate that system‐level sampling efforts intended to inform microbial risk assessments must account for variability in indicators of risk over time.